Generalized two-sided linear prediction approach for land mine detection

Chan, Tsz-King; So, Hing-Cheung; Ho, K.C.

doi:10.1016/j.sigpro.2007.10.008

Cited by 12 publications

(11 citation statements)

References 11 publications

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“…During scanning, it is aimed to obtain a DWS around buried object to mark suspicious region. For this purpose, DTS is needed [9]. This information can be used to give an audio-visual warning to the operator, in the next step.…”

Section: Buried Object Detection Problemmentioning

confidence: 99%

“…But, in the hard cases like in Figure 2b, we may not have enough thresholding range to mark buried object location without false alarms, most of the time. The proposed detection test statistic: ADF [24] is defined by (7), (8) and (9). It uses first-and secondorder moments of target energy function and is calculated through a sliding window.…”

Section: Auxiliary Detection Functionmentioning

confidence: 99%

“…Identification processes [2][3][4][5][6][7] can be applied after the buried object location is determined. There are numerous methods to detect buried objects utilizing GPR; linear prediction [8][9][10], principal component analysis [11,12], independent component analysis [11], wavelet domain [13], frequency domain correlation [14,15], time domain correlation [16], linear minimum mean square error estimation, [17], Gumbel distribution [18], and least square-based [19] methods can be given in this scope.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

Sezgin

2011

EURASIP J. Adv. Signal Process.

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In this article, a simultaneous buried object detection and imaging method is proposed for time domain ground penetrating radar (GPR) data. Fuzzy weighted background removal is applied to the data through a sliding window and then target energy functions are obtained by means of convolution summations of consecutive A-scan signals in an appropriate manner. An auxiliary detection function is proposed as an emphasized detection test statistic and then an automatic detection warning signal creation method is devised. The proposed method has been tested over a set of small-sized surrogate anti-personnel (AP) mines which are not easily detectable and medium-sized surrogate AP and Anti-tank mines. The results are promising as nearly full detection performance. Zero false alarm rate is achieved in this dataset without remarkable corruption in estimated target GPR images. Moreover, it is observed that the noise immunity of the proposed method is highly satisfactory in terms of detection probability.

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Section: Buried Object Detection Problemmentioning

confidence: 99%

Section: Auxiliary Detection Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

Sezgin

2011

EURASIP J. Adv. Signal Process.

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“…PIM and RM are useful for locating metallic objects. Moreover, GPR has shown greater ability in detecting non-metallic pipes comparing with the above methods [8,34], as well as inspecting and mapping other types of buried objects such as land mines [7] and archeologic vestiges [9], among others. However, even though information retrieval by GPR is worthwhile, the huge volume of generated information and the interpretation of data usually require high levels of skill and experience [35].…”

Section: Introductionmentioning

confidence: 99%

Location of buried plastic pipes using multi-agent support based on GPR images

Ayala-Cabrera

Herrera

Izquierdo

et al. 2011

Journal of Applied Geophysics

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This work focuses on the generation of tools to aid inspection and identify buried plastic pipes in water supply systems (WSS). In our study we use ground penetrating system (GPR) images as a non-destructive method of obtaining information without altering the system conditions and the environmental characteristics. A viability study for extracting features, and an approach to the above-mentioned application based on multi-agent systems are addressed in this paper. Firstly, we use intensive matrix manipulation of the GPR output for preprocessing the images. As a result, two matrices are produced that classify initial data based on the original radargram of the wave amplitude parameter. Then the plastic pipe characteristics that offer an enhanced likelihood of location are defined. This procedure is evaluated through two case-studies. One study corresponds to a simple case (one pipe) and the other corresponds to various pipes (made of different materials). Both cases were developed under controlled laboratory conditions. The obtained results are promising, and we show that automatic plastic pipe location has been achieved. The main contributions of the procedures proposed in this work are: firstly, highly skilled GPR prospection operators become unnecessary for plastic pipe location using GPR images; and secondly, we have opened a route to further classification that makes use of other methodologies.

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“…There have been interesting works on GPR detection in landmine searching [Gader et al (2000); Chan et al (2007)], archaeological exploration [Cukavac et al (2008)], and steel for the reinforcement of concrete [Shaw et al (2005)], among others.…”

Section: Introduction To Methodologies Of Visualization Of Objects Inmentioning

confidence: 99%

Characterization of components of water supply systems from GPR images and tools of intelligent data analysis.

Ayala-Cabrera¹

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Dedico este trabajo doctoral:En primera instancia a Dios.A mis padres, David Ayala y Nirza Cabrera, porque es por ustedes que soy lo que soy. Gracias por brindarme el apoyo y las bases para sacar mis proyectos adelante. Gracias por todo el cariño y por todos los esfuerzos que han y siguen realizando por mí.A mi adorada esposa, Anastasia Tsitsou, gracias por el apoyo incondicional brindado, por todos los momentos sacrificados y especialmente por los bellos momentos a tu lado, por ser mi motivo de inspiración y mis deseos de superación. Gracias por todo el amor incondicional ofrecido. Mil y mil gracias Agapi mou.Finalmente, a mis hermanos Ramier, Pilar, Patricia y Katerine. AgradecimientosEste trabajo ha sido posible gracias al Programa de Ayudas de Investigación y Desarrollo (PAID) de la Universitat Politècnica de València, que han concedido una beca para el autor, en el marco del Programa para la Formación de Personal Investigador (FPI) para el año 2011.Quiero agradecer a mis directores Rafael Pérez García, Joaquín Izquierdo y Manuel Herrera por creer en este proyecto, por su apoyo, por sus buenos consejos tanto en lo emocional como en lo profesional. Adicionalmente, gracias por ser esa chispa que inició mis caminos en la investigación. A Rafael gracias por su apoyo y constancia y por llevarme por ese tan inexplorado camino de los métodos no destructivos. A Joaquín mis agradecimientos por su entrega y su dedicación, su apoyo y comprensión, gracias por esos inesperados artículos a publicar que medían mi capacidad de reacción. Gracias por toda la dedicación a los incomprensibles momentos de mi creatividad.A Manuel, gracias por todo su apoyo y amistad, por incentivarme a publicar y así compartir mis ideas, gracias por ser ese amigo y colega cómplice en el desarrollo de mi trabajo y por sus invaluables consejos. De igual manera quiero hacer extensivo este agradecimiento a todos los integrantes de FluIng, especialmente a Joanna, Eva, Enrique e Idel.Quiero agradecer a la National Technical University of Athens de Grecia, específicamente al grupo de investigación Microwave and Fiber Optics Laboratory, por acogerme en mi periodo de estancia. Particularmente, quiero agradecer a Manthos Bimpas por su apoyo, amistad y buenos consejos en el ámbito profesional, además de hacer posible el desarrollo de esta estancia. Manthos, su ejemplo de trabajo bien realizado es motivador, Efcharisto Poli.Mis agradecimientos para el Consorcio AQUARUM por su ayuda en la captura de datos de campo. En especial a los ingenieros Mario Tavera y Halmark Mejía.Quiero manifestar mi enorme gratitud a los ingenieros Beatriz Wills y Guillermo Chica de la Universidad de Antioquia, con quienes siempre he podido contar con sus valiosos consejos y amistad. Así mismo, hacer extensivo mi gratitud a mis excompañeros de Ingedelta, Wilmar, Mauricio, Andrés y Jorge, quienes me apoyaron desde los inicios del doctorado. A los Ingenieros Camilo Cruz y Patricia Torres de la Universidad delValle, gracias por brindarme esa mano amiga.Gracias a mis amigos Paola Nates,...

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Generalized two-sided linear prediction approach for land mine detection

Cited by 12 publications

References 11 publications

Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data

Location of buried plastic pipes using multi-agent support based on GPR images

Characterization of components of water supply systems from GPR images and tools of intelligent data analysis.

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